Deceased Humpback Whale - Joshua Meza-Fidalgo, 2020

Deceased Humpback Whale - Joshua Meza-Fidalgo, 2020

Rationale and Research Questions

Marine mammal and sea turtle strandings can be unusual events or indicators of problems in our ocean ecosystems. Seeing patterns in the strandings of marine mammals and sea turtles can be indicative of more complex issues that may not be transparent. Some examples of these issues could be viral outbreaks in lower trophic level populations or toxic algal blooms that kill off all sea life in the area. Because humans demand and consume seafood, understanding the problems that arise are essential to preventing the consumption of contaminated seafood along with the management of fisheries. By studying marine mammal and sea turtle strandings, we get insight on the health of our oceans.

Our objective is to analyze animal strandings over the years to see if there any trends in the quantity of strandings for each family (cetaceans, odontocetes, mysticetes, and pinnipeds). We have decided to look at the total number of strandings per year for all families, the changes in strandings over the years for each family and the seasonality differences across all years for each family.


Dataset Information


Table 1: Dataset Information
Detail Description
Data Source OBIS-SEAMAP - Mystic Aquarium
Retrieved From https://seamap.env.duke.edu/dataset/945
Variables Used Family, Common Name, Species Name, Date, Year, Month, Latitude, Longitude, Total Strandings, Strandings by Species
Data Range March 18, 1976 to December, 29, 2011


The dataset that we chose to work with is the Mystic Aquarium’s marine mammal and sea turtle stranding data that started in 1976 until 2011. However, there was only one stranding record in 1976, so that point was excluded from our analysis. The rest of the data started in 1990 and was concluded in 2011. It contains the number of whale (odontocetes and mysticetes), pinniped (seals and sea lions), and sea turtle strandings that occurred along the coasts of Connecticut, Rhode Island and Fishers Island, New York, USA. There were a total of 1140 strandings; within these strandings, 155 were odontocetes, 50 were mysticetes, 679 were pinnipeds, and 256 were sea turtles.

The dataset has a latitude that ranges from 41.00 to 41.86 while the longitude ranges from -73.65 to -71.10. The data was provided using the datum WGS 1984 (4326) and we performed a data transformation into UTM 19 (32619) for geospatial analysis. The data was wrangled to contain only the variables that we were interested in using. We then selected parts of the data based off of the Common Name of the species to create datasets that were easier for us to use in our analysis. This helped us divide the data into four groups: odontocetes, mysticetes, pinnipeds, and sea turtles. We initially looked at total yearly strandings for trends in the data for each family. After we analyzed the data on a monthly basis to help give insight to seasonal stranding trends for each family.


Exploratory Analysis

We initially decided that it would be best to look at the data holistically to get a better feel for the trends in the data. We first analyzed the total number of strandings per each family, as seen in Figure 1. To better see the data, we divided and summed the strandings of each family for each year from 1990 to 2011, as seen in Figure 2. To see if there could be seasonal trends for the entire data set, the data was grouped by month for each family from the years 1990 to 2011, as seen in Figure 3. Finally, to have a geospatial understanding of our data, Map 1 was created.


Figure 1: Total Strandings per Family


Figure 2: Total Strandings per Year


Figure 3: Total Strandings per Month


Map 1: Geospatial Exploration of each Family


Analysis

Because we decided to divide the data into groups by their families, we conducted separate analysis for each of the families. Our research questions are the following:

1. Are there years or months where the number of strandings for each family are significantly different?
2. How are strandings related spatially? Are there areas with higher concentrations of strandings than others?
3. Are there trends in each of the family strandings based off this data set?

To answer our first question, we used a GLM for our statistical analysis. Due to our data being count data rather than continuous, we used a poisson regression GLM. To answer our second question, we found the mean latitude and longitude for each family. We then calculated how far each stranding point was from the mean location to determine if strandings were concentrated to certain areas. To answer our third question, we conducted a yearly time series with the Mann Kendall and a monthly time series with the Seasonal Mann Kendall.

1. Pinnipeds:

Pinniped Data Exploration

Figure 4: Total Pinniped Strandings per Year by Species


Figure 5: Total Pinniped Strandings per Month by Species


Pinniped data exploration indicates a possible overall increasing trend in pinniped strandings and a possible monthly/seasonal trend. There is some fluctuation in number of total yearly strandings both for the family as a whole and at the species level (Figure 4). Harp and harbor seals make up most of the strandings each year. Most monthly strandings occur in the late winter and spring time, particularly in March, with very few in the late summer through the early winter. Springtime coincides with the pupping seasons for many seal species and could be a factor in this. This increase in late winter through springtime strandings can be seen in Figure 5. Figure 6 shows an some fluctuation, but an overall increase in pinniped strandings over time. Pinniped strandings are most numerous throughout the coastline of Rhode Island and the eastern coastline of Connecticut, as seen in Map 2 below.


Figure 6: Pinniped Strandings per Year



Map 2: Geospatial Exploration of Pinniped Strandings


Pinniped Stranding Statistical Analysis

The following are the null and alternative hypothesis for the annual and monthly statistical analysis using Poisson Regression (GLM):

Null Hypothesis 1: There is no effect of year on the total number of pinniped strandings.
Alternative Hypothesis 1: There is an effect of year on the total number of pinniped strandings.

Null Hypothesis 2: There is no effect of month on the total number of pinniped strandings.
Alternative Hypothesis 2: There is an effect of month on the total number of pinniped strandings.

Yearly Analysis:
We reject the null hypothesis that there is no effect of year on the total number of pinniped strandings. 18 of the 21 documented years were statistically significant with p-values ranging from 9.07e-09(2001) to 0.03(1999). Only the first three years of data, 1991-1993 had p-values above 0.05. The null deviance of yearly model was 1.36e+02 on 21 degrees of freedom, and residual deviance of 1.31e-14

Monthly Analysis:
We reject the null hypothesis that there is no effect of month on the total number of pinniped strandings. 11 of the 12 months had p-values of less than 0.05, with July being the exception. The p-values of the statistically significant month ranged from 1.77e-15(March) to 0.02(June). The null deviance for the monthly model was 5.95e+02 on 11 degrees of freedom and a residual deviance of -3.55e-15.



Pinniped Geospatial Analysis


Map 3: Pinniped Distance (m) from Mean Stranding Point


Our geospatial analysis showed that 425 out of 679 (62.5%) pinniped strandings occurred within 40km of the mean stranding location and 200 (29.5%) occurred within 60km. The remaining 54 (8%) pinniped strandings occurred farther than 60km from the mean stranding location. These values can be seen in Table 2 below. The majority of pinniped strandings occurred from Charlestown to Newport in the Rhode Island Sound (Map 3).


Table 2: Number of Pinniped Strandings per Distance (km) from the Mean
Distance (km) Number of Strandings
20 - 40 425
40 - 60 200
60 - 80 15
80 - 100 11
100 - 120 7
120 - 140 11
140 - 160 7
greater than 160 3


Pinniped Temporal Analysis

The following are the null and alternative hypothesis for the yearly and monthly time series:

Null Hypothesis: The yearly and/or monthly/seasonal pinniped stranding data is stationary.
Alternative Hypothesis: The yearly and/or monthly/seasonal pinniped stranding data is not stationary.


Figure 7: Pinniped Monthly Decomposition


Based on the results from the Mann Kendall test, we reject the null hypothesis that yearly pinniped strandings are stationary with a p-value of 1.77e-14.

Based on the results from the Seasonal Mann Kendall test, we reject the null hypothesis that monthly pinniped strandings are stationary, with a p-value of 7.80e-6. The decomposition indicates a general upward trend as well as seasonality, which fits with the patterns that were seen in the exploratory analysis (Figure 7).


2. Odontocetes:

Odontocete Data Exploration

Figure 8: Total Odontocete Strandings per Year by Species


Figure 9: Total Odontocete Strandings per Month by Species


There appears to be an increasing trend in the total number of odontocete strandings as the years progress (Figure 8). The amount of odontocete strandings was lowest in 1993 and highest in 2011, where the count gradually increases over the years. The different species that stranded throughout the years do not appear to have a noticeable trend. When looking at the strandings per month in Figure 9, the months with the most strandings are March, May, and July. There may be possible seasonal trends due to the calving season in spring and early summer. When looking at Figure 10 below, there is a visible increasing trend in the number of strandings of odontocetes over the years. All of these strandings can be seen in the Map 4 below.


Figure 10: Odontocete Strandings per Year


Map 4: Geospatial Exploration of Odontocete Strandings


Odontocete Stranding Statistical Analysis

The following are the null and alternative hypothesis for the annual and monthly statistical analysis using Poisson Models (GLM):

Null Hypothesis 1: There is no effect of individual year on the total number of odontocete strandings.
Alternative Hypothesis 1: There is an effect of year on the total number of odontocete strandings.

Null Hypothesis 2: There is no effect of month on the total number of odontocete strandings.
Alternative Hypothesis 2: There is an effect of month on the total number of odontocete strandings.

Yearly Analysis:
We reject the null hypothesis that there is no effect of year on the total number of odontocete strandings. Of all the years in the odontocete strandings, only 2 years are statistically significant; 1990 and 2011 were both statistically significant with p-values of 1.14e-05 and 0.0198 respectively. The null deviance was had a value of 4.0129e+01 and the residual deviance had a value of. -2.1867e-21.

Monthly Analysis:
We reject the null hypothesis that there is no effect of month on the total number of odontocete strandings. There were four months in which the odonotocete strandings were statistically significant: January, March, May, and June. The most statistucally significant months were January and May with p-values of 4.35e-11 and 0.00858 respectively. March had a p-value of 0.02389 and June had a p-value of 0.03344. The null deviance was 3.7282e+01 and the residual deviance was -3.5527e-15.


Odontocete Geospatial Analysis


Map 5: Odontocete Distance (m) from Mean Stranding Point


Of the 155 odontocete strandings used in our analysis, 125 (80.1%) of them were within 40km of the mean stranding location. This was followed by 20 (12.9%) strandings that were within 40km to 60km from the mean stranding location. The remaining 10 (6.5%) strandings were scattered farther than 60km, and there was one stranding that was greater than 180km from the mean stranding location. These values can be seen in Table 3 above. The majority of strandings occurred from Charlestown to Newport in the Rhode Island Sound (Map 5). Most of the strandings were centered near Newport and many were also located up in Narragansett Bay.


Table 3: Number of Odontocete Strandings per Distance (km) from the Mean
Distance (km) Number of Odontocete Strandings
less than 40 125
40 - 60 20
60 - 80 1
80 - 100 0
100 - 120 3
120 - 140 2
140 - 160 1
160 - 180 2
greater than 180 1



Odontocete Temporal Analysis

The following are the null and alternative hypothesis for the yearly and monthly time series:

Null Hypothesis: The yearly and/or monthly/seasonal odontocete stranding data is stationary.
Alternative Hypothesis: The yearly and/or monthly/seasonal odontocete stranding data is not stationary.


Figure 11: Odontocete Monthly Decomposition


Based on the results of the Mann Kendall test, we reject the null hypothesis and say that odontocete yearly stranding data is not stationary due to a p-value of 4.4296e-13.

Based on the results from the Seasonal Mann Kendall test, we reject the null hypothesis and say that odontocetes monthly stranding data is not stationary due to a p-value of 1.3611e-07. This can also be seen in our decomposition where there appears to be a seasonal trend in the data over the years (Figure 11).


3. Mysticetes

Mysticete Data Exploration

Figure 12: Total Mysticete Strandings per Year by Species


Figure 13: Total Mysticete Strandings per Month by Species


There appears to be an increasing trend in the number of mysticete strandings in the later years of the study (Figure 12). The year with the highest number of strandings was 2004, followed by 2009. There was variation in which species were stranded in each year. When looking at Figure 13, there also appears to be a seasonal trend based on the number of strandings per each month, where the months of June and July have a much greater amount than the rest of the year. There may be possible seasonal trends due to higher concentrations of animals in the area in the early summer due to migration. When looking at Figure 14 below, there is a slightly increasing trend in the number of strandings of mysticetes over the years. It appears that the number of strandings is consistent until 2000, where the numbers increase before dropping down again in 2009. All of these strandings can be seen in Map 6 below.


Figure 14: Mysticete Strandings per Year



Map 6: Geospatial Exploration of Mysticete Strandings


Mysticete Stranding Statistical Analysis

The following are the null and alternative hypothesis for the annual and monthly statistical analysis using Poisson Models (GLM):

Null Hypothesis 1: There is no effect of year on the total number of mysticete strandings.
Alternative Hypothesis 1: There is an effect of year on the total number of mysticete strandings.

Null Hypothesis 2: There is no effect of month on the total number of mysticete strandings.
Alternative Hypothesis 2: There is an effect of month on the total number of mysticete strandings.

Yearly Analysis:
We reject the null hypothesis that there is no effect of year on the total number of mysticete strandings due to none of the p-values in each year being below 0.05. This means that overall, there is no change in the number of strandings over the years of the study. The null deviance had a p-value of 2.1360e+01, and the residual deviance had a p-value of 4.1223e-10.

Monthly Analysis:
We fail to reject the null hypothesis that there is no effect of month on the total number of mysticete strandings. There were two months in which the mysticete strandings were statistically significant: May and June. The most statistically significant month was May with a p-value of 0.00874 while June had a p-value of 0.01345. The null deviance had a p-value of 5.7723e+01 the residual deviance had a p-value of 3.0330e-10.


Mysticete Geospatial Analysis


Map 7: Mysticete Distance (m) from Mean Stranding Point


Of the 50 mysticete strandings used in our analysis, 44 (88%) of them were within 40km of mean stranding location. This was followed by 5 (10%) strandings that were within 40km to 60km from the mean stranding location. The remaining stranding was located between 140km to 160km away from the mean stranding location. These values can be seen in Table 4 below. The majority of strandings occurred from Stonington to Newport in the Rhode Island Sound. Most were centered near Newport and some were located in Narragansett Bay (Map 7).



Table 4: Number of Mysticete Strandings per Distance (km) from the Mean
Distance (km) Number of Strandings
less than 40 44
40 - 60 5
60 - 80 0
80 - 100 0
100 - 120 0
120 - 140 0
140 - 160 1



Mysticete Temporal Analysis

The following are the null and alternative hypothesis for the yearly and monthly time series:

Null Hypothesis: The yearly and/or monthly/seasonal mysticete stranding data is stationary.
Alternative Hypothesis: The yearly and/or monthly/seasonal mysticete stranding data is not stationary.


Figure 15: Mysticete Monthly Decomposition


Based on the results from the Mann Kendall test, we reject the null hypothesis and say that mysticete yearly stranding data is not stationary due to a p-value of less than 2.22e-16.

Based on the results from the Seasonal Mann Kendall test, we reject the null hypothesis and say that mysticetes monthly stranding data is not stationary due to a p-value of 0.014425. This can also be seen in our decomposition where there appears to be a seasonal trend in the data over the years (Figure 15).


4. Sea Turtles

Sea Turtle Data Exploration


Figure 16: Total Sea Turtle Strandings per Year by Species


Figure 17: Total Sea Turtle Strandings per Month by Species


The highest amounts of sea turtles strandings occurred in the early 1990s and late 2000s, with the highest amount in 1995 (Figure 16). The most prominent species throughout our study period appear to be leatherback sea turtles, followed by loggerhead sea turtles. Monthly data exploration indicates that sea turtle strandings peak from July to September and occur less frequently the remainder of the year (Figure 17). This could be due to higher concentrations of sea turtles in the area for the summer nesting season. When looking at Figure 18 below, there is a very slight increasing trend in the number of strandings of sea turtles over the years. All of the sea turtle strandings can be seen in Map 8 below.


Figure 18: Sea Turtle Strandings per Year



Map 8: Geospatial Exploration of Sea Turtle Strandings


Sea Turtle Statistical Analysis

The following are the null and alternative hypothesis for the annual and monthly statistical analysis using Poisson Models (GLM):

Null Hypothesis 1: There is no effect of individual year on sea turtle strandings.
Alternative Hypothesis 1: There is an effect of individual year on sea turtle strandings.

Null Hypothesis 2: There is no effect of month on sea turtle strandings.
Alternative Hypothesis 2: There is an effect of month on sea turtle strandings.

Yearly Analysis:
We reject the null hypothesis that individual year has no effect on sea turtle strandings. The sea turtle strandings in 9 of the 21 years are not significantly different (p-values: 0.147 - 0.706). 1995 was the most significantly different (p-value: 0.000236). 1991 (p-value: 0.0056), 1993 (p-value: 0.0078), 2008 (p-value: 0.0022), 2010 (p-value: 0.0016), and 2011 (p-value: 0.003) were the next most statistically different years. The null deviance of this model is 7.4606e+01 and the residual deviance is -4.8850e-15.

Monthly Analysis:
We reject the null hypothesis that month has no effect on sea turtle strandings. Of the 12 months, 5 of months were significantly different. These months include May (p-value: 0.000269), June (p-value: 3.98e-06), July (p-value: 1.91e-05), August (p-value: 0.004106), and September (p-value: 0.049935). This indicates that sea turtle strandings are concentrated to the summer months in our study area. The null deviance of this model is 4.9235e+02 and the residual deviance is 3.0332e-10.


Sea Turtle Geospatial Analysis


Map 9: Sea Turtle Distance (m) from Mean Strandign Point


Of the 256 sea turtle strandings used in our analysis, 92 (36%) were within 20km of the mean stranding location and 113 (44%) were within 20 to 40km of the mean stranding location. These values can be seen in Table 5 below. These points were in the areas around Charlestown, Newport, and New Shoreham. Some sea turtle strandings were reported inland, which could be due to tides/currents bringing the animal upstream into rivers (Map 9).



Table 5: Number of Sea Turtle Strandings per Distance (km) from the Mean
Distance (km) Number of Strandings
less than 20 92
20 - 40 113
40 - 60 32
60 - 80 2
80 - 100 4
100 - 120 2
120 - 140 6
140 - 160 4
greater than 160 1


Sea Turtle Temporal Analysis

The following are the null and alternative hypothesis for the yearly and monthly time series:

Null Hypothesis: The yearly and/or monthly/seasonal turtle stranding data is stationary.
Alternative Hypothesis: The yearly and/or monthly/seasonal turtle stranding data is not stationary.


Figure 19: Sea Turtle Decomposition


Based on the results from the Mann Kendall test, we reject the null hypothesis that yearly turtle strandings are stationary with a p-value =< 2.22e-16.

Based on the results from the Seasonal Mann Kendall test, we fail to reject the null hypothesis that monthly turtle strandings are stationary with a p-value = 0.270. This can also be seen in our decomposition where there appears to be a seasonal trend in the data over the years (Figure 19).


Summary

1. Pinnipeds Summary:

  1. 85.7% of the years and 11 of the 12 months were statistically significant for pinniped strandings.
  2. 62.5% of pinniped strandings occurred within 40km of the mean stranding point.
  3. Time series analysis indicated that there was an increasing trend in the yearly totals of pinniped strandings. The monthly time series indicated that there was a slight upward monthly trend along with seasonality, which could be attributed to pupping seasons.

2. Odontocetes Summary:

  1. The years 1990 and 2011 were statistically significant for odontocete strandings. There was also a significant difference in the number of strandings during the months of January, March, May, and June. The increased number of strandings during the earlier months may be contributed to calving season or seasonal migrations.
  2. We found that 80.6% of our strandings were within 40km of the mean stranding point and 93.5% fell within 60km of the mean stranding point.
  3. The time series indicated that odontocete strandings were not stationary over the years or the months.

3. Mysticetes Summary:

  1. None of the years of our study were statistically significant for mysticetes, which meant that overall, there was no major difference between the number of strandings over the study period. There was a significant difference in the number of strandings during the months of May and June.
  2. We found that 88% of our strandings were within 40km of the mean stranding point and 98% fell within 60km.
  3. The time series indicated that mysticete strandings were not stationary over the years or the months. The increase in the number of strandings over the years indicate that there may be changes in the environment over the study period. The increased strandings during the months of May and June may be contributed to seasonal migrations.

4. Sea Turtles Summary:

  1. 47.6% of years were statistically significant for sea turtle strandings. 41.7% of months were statistically significant, which were the May through September.
  2. 80% of sea turtles strandings all occurred within 40km of the mean stranding location.
  3. Time series analysis indicated that there was a slight significance in year and there was not a significant seasonal component for sea turtle strandings. While our data exploration showed that most of the strandings occurred in the summer months, this could be due to certain months of certain years having disproportionately higher amounts of strandings and could be the reason seasonality was not significant in our sea turtle data.

Conclusions

Our results show that marine mammal and sea turtle strandings have varied throughout 1990 to 2011 along the coastlines of Connecticut, Rhode Island, and Fishers Island, NY. The years and months that were statistically significant varied by family. The majority of months that were significant can be related back to a period necessary for specific life history stages, such as the pinniped pupping season, odontocete calving season, mysticete migration and feeding season, and sea turtle nesting season. Higher abundances of animals are present in the area around these times of year which could lead to higher amounts of detected strandings.

Similarly, our results show that the majority of strandings for each family is concentrated to areas within 40 to 60km of the mean stranding location, with most strandings of all families concentrated in the waters surrounding Charleston, RI to Newport RI. The high aggregation of strandings in these areas could be a result of various factors, such as…

However, without background information on the cause of death of each individual stranding, it is not possible to narrow down which, if any, of the above factors are contributing to strandings and in what capacities.

Yearly and seasonal trends varied across the families. Each family’s yearly trend increased throughout the study period, with some having a more drastic increase than others. Only pinnipeds, odontocetes, and mysticetes had a significant seasonal component, which could relate back to their respective breeding or feeding/migration seasons.

Understanding trends in marine mammal and sea turtle strandings is a crucial aspect of the health of our ocean ecosystems. The general increasing trend of strandings throughout the years is alarming and could be attributed to various natural and anthropogenic factors. Insight into how strandings change monthly and annually, as well as spatially, is necessary for stranding prevention and response, resource and personnel allocation, and conservation management. Future studies should incorporate environmental factors into their analysis to develop methodologies to help predict, prevent, or mitigate potential strandings. Additionally, future work could incorporate species-level analysis, as well as cause of death to help pinpoint the most vulnerable species and the most common threats.


Deceased Fin Whale - Joshua Meza-Fidalgo, 2020

Deceased Fin Whale - Joshua Meza-Fidalgo, 2020

References

Data: https://seamap.env.duke.edu/dataset/945

  1. Halpin, P.N., A.J. Read, E. Fujioka, B.D. Best, B. Donnelly, L.J. Hazen, C. Kot, K. Urian, E. LaBrecque, A. Dimatteo, J. Cleary, C. Good, L.B. Crowder, and K.D. Hyrenbach. 2009. OBIS-SEAMAP: The world data center for marine mammal, sea bird, and sea turtle distributions. Oceanography. 22(2):104-115.

  2. Smith, A. 2014. Mystic Aquarium’s marine mammal and sea turtle stranding data 1976-2011. Data downloaded from OBIS-SEAMAP (http://seamap.env.duke.edu/dataset/945) on 2022-04-02.

Photos: Special thanks to Joshua Meza-Fidalgo for providing whale photos for our report.